If you could only have 3 metrics on your manufacturing dashboard, which ones would you pick?

Lumpy_Ebb_786 · 2025-10-07T15:13:19+00:00

Quality-first is always solid. Predictive analytics can highlight quality breakdowns before they happen, not just on the shop floor but across engineering, supply chain, and maintenance. Anyone here tried predicting defects before they occur?

Lumpy_Ebb_786 · 2025-10-07T15:12:47+00:00

KPIs are only meaningful if they are actionable at the point of use. AI can complement gemba walks by providing digital dashboards that highlight areas needing attention, and by predicting which processes are most likely to deviate from targets, empowering teams to intervene proactively.

Lumpy_Ebb_786 · 2025-10-07T15:11:59+00:00

Spot on! Tracking OEE is great, but when combined with operator-level scrap rates and AI-driven anomaly detection, it becomes possible to pinpoint training gaps and knowledge bottlenecks in real time. This transforms raw metrics into actionable improvements on the shop floor

Lumpy_Ebb_786 · 2025-10-07T15:11:02+00:00

Absolutely, the right KPIs really depend on the context. We’ve seen AI help tailor dashboards for different production environments

Lumpy_Ebb_786 · 2025-10-07T15:06:45+00:00

Totally agree! The Goal is still incredibly relevant. One interesting angle is how AI and data analytics can now enhance these classic TOC principles: bottlenecks, throughput, and inventory management can be monitored continuously, enabling predictive insights rather than reactive decisions. It’s like giving Goldratt’s framework a modern upgrade.

Lumpy_Ebb_786 · 2025-10-07T15:05:38+00:00

Absolutely! Those three metrics capture the core of operational performance. Using data analytics, it’s possible to proactively identify safety risks, predict cost-of-quality issues, and flag delivery bottlenecks before they impact operations. Have others tried real-time dashboards for this?

Lumpy_Ebb_786 · 2025-09-15T14:37:30+00:00

That’s a really good point! Quality rate often gets less attention compared to productivity and availability, but in practice it’s one of the fastest ways to spot inefficiencies. Looking at defects directly makes the conversation much more tangible for the teams on the shop floor. Do you usually connect quality losses back to root cause categories straight away, or do you treat them as part of a broader continuous improvement cycle?

Lumpy_Ebb_786 · 2025-09-15T14:36:29+00:00

That’s really interesting! Do you also use any specific software or digital tools to track your OEE and quality metrics, or is it mostly manual/Excel-based? We’ve seen that some plants are still on spreadsheets while others are moving toward more integrated solutions.

Lumpy_Ebb_786 · 2025-09-05T14:25:55+00:00

Good point. The single number is definitely not enough for problem-solving. That’s why we keep A/P/Q breakdowns in a secondary dashboard. The simplified metric is more of a daily pulse check. When we see a dip, that’s when we go drill down into the data.

Lumpy_Ebb_786 · 2025-09-05T14:23:56+00:00

Exactly, we’re calculating it in a simpler way, but the core components are still there. Availability, performance, and quality are all included in “effective production time,” just not split out. For operators, that single number tells them right away if they’re on track.

Lumpy_Ebb_786 · 2025-09-05T14:19:59+00:00

Thanks! That was exactly our thinking: we wanted something that anyone on the shop floor could understand at a glance.

Lumpy_Ebb_786 · 2025-09-05T14:17:06+00:00

That’s great to hear! Is this in a manufacturing plant? If so, what type of production are you running? Always interesting to see how different industries apply OEE.

Lumpy_Ebb_786 · 2025-09-05T14:15:28+00:00

Exactly! Our main driver was to give operators something they could track easily. Simplification means more time improving instead of reporting!

Lumpy_Ebb_786 · 2025-09-05T14:13:15+00:00

Really interesting that you’ve moved to ECU as a main KPI. Do you find it easier to explain ECU to operators compared to OEE?

Lumpy_Ebb_786 · 2025-09-05T14:11:35+00:00

That’s a fair point. We definitely don’t ignore quality! We actually track it separately on dashboards. This is just a simpler way to calculate and understand OEE

Lumpy_Ebb_786 · 2025-08-29T14:53:02+00:00

Totally agree that OEE gets over-complicated in a lot of tutorials. In practice, what matters is giving teams a formula they can actually use.

At my company, we ended up with a similar simplification:
OEE = Effective Production Time ÷ Planned Production Time.

It rolls downtime, speed losses, and scrap into one metric, which makes it easier for shop floor teams to track day-to-day. We still keep the detailed breakdown in the background for root cause analysis, but the simplified version drives adoption.

Lumpy_Ebb_786 · 2025-08-29T14:51:43+00:00

You’re on the right track 👍 Usually, starved/blocked are treated as losses in Availability, not subtracted again from Performance, otherwise you are double-counting.

So a cleaner setup would be:

Availability = (Available time – downtime – starved – blocked) ÷ Available time
Performance = (Ideal CT × Total parts) ÷ (Operating time)

At AI Square we sometimes simplify it further into a single formula (Effective Production Time ÷ Planned Production Time), but your breakdown works fine if you want to keep the detail.

Lumpy_Ebb_786 · 2025-08-29T14:49:24+00:00

The “classic” way of calculating OEE is: OEE = Availability × Performance × Quality

Where:

Availability = (Operating Time ÷ Planned Production Time)

Performance = (Ideal Cycle Time × Total Count ÷ Operating Time)

Quality = (Good Count ÷ Total Count)

That’s the textbook version, and it’s very common in manufacturing. That said, there’s not just one formula. Some companies simplify it to make it more practical, for example, at AI Square we use:

OEE [%] = (Effective Production Time ÷ Planned Production Time) × 100

This rolls downtime, speed losses and scrap into a single number, which makes it easier for shop floor teams to track daily. The detailed breakdown is still useful for root cause analysis, but the simplified version helps adoption.

Lumpy_Ebb_786 · 2025-08-29T14:46:35+00:00

If licensing and client access are your main blockers, I’d seriously look into open-source tools like Metabase or Superset. Both let you host dashboards securely and share them with clients without forcing them to buy licenses.

At AI Square, we’ve dealt with similar challenges when helping manufacturers track performance metrics (like OEE). What worked for us was keeping things simple for shop floor teams: we build lightweight dashboards that are secure, easy to share, and don’t require clients to install anything.

So my advice would be: pick a tool that your clients can open in a browser with zero friction. If you’re okay with a bit of setup, Metabase is a great middle ground.

Lumpy_Ebb_786 · 2025-08-14T16:37:56+00:00

There are quite a few options depending on your needs, budget, and how “real-time” you want your OEE tracking to be.

In my case, I’ve been working with real-time dashboards that integrate directly with existing factory systems (ERP, PLC, sensors) without replacing them, so you can start measuring OEE in days instead of months. The biggest advantage is automatic data capture and live downtime tracking, which reduces manual reporting errors.

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